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Pattern Recognition Letters
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
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EURASIP Journal on Advances in Signal Processing
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Pattern Recognition Letters
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AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
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IEEE Transactions on Fuzzy Systems
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ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Multi-scale feature density approximation for object representation and tracking
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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Multimedia Tools and Applications
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MUSP'10 Proceedings of the 10th WSEAS international conference on Multimedia systems & signal processing
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Computer Vision and Image Understanding
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Expert Systems with Applications: An International Journal
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Computer Vision and Image Understanding
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PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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Pattern Recognition Letters
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Artificial Intelligence Review
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Applied Intelligence
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Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Adaptive Gaussian mixtures have been used for modeling nonstationary temporal distributions of pixels in video surveillance applications. However, a common problem for this approach is balancing between model convergence speed and stability. This paper proposes an effective scheme to improve the convergence rate without compromising model stability. This is achieved by replacing the global, static retention factor with an adaptive learning rate calculated for each Gaussian at every frame. Significant improvements are shown on both synthetic and real video data. Incorporating this algorithm into a statistical framework for background subtraction leads to an improved segmentation performance compared to a standard method.